| 基于可解释性机器学习的城市O3驱动因素挖掘 |
| 摘要点击 2134 全文点击 525 投稿时间：2022-08-23 修订日期：2022-10-11 |
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| 中文关键词 O3污染 驱动因素 影响贡献 可解释性 机器学习 |
| 英文关键词 O3 pollution driving factors contribution explanation machine learning |
| 作者 | 单位 | E-mail | | 董佳奇 | 华北电力大学环境科学与工程学院, 资源环境系统优化教育部重点实验室, 北京 102206 | dongjiaqi972022@163.com | | 胡冬梅 | 华北电力大学环境科学与工程学院, 资源环境系统优化教育部重点实验室, 北京 102206 | huhu3057@163.com | | 闫雨龙 | 北京交通大学环境学院, 北京 100044 | | | 彭林 | 北京交通大学环境学院, 北京 100044 | | | 张鹏辉 | 华北电力大学环境科学与工程学院, 资源环境系统优化教育部重点实验室, 北京 102206 | | | 牛月圆 | 华北电力大学环境科学与工程学院, 资源环境系统优化教育部重点实验室, 北京 102206 | | | 段小琳 | 华北电力大学环境科学与工程学院, 资源环境系统优化教育部重点实验室, 北京 102206 | |
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| 中文摘要 |
| 受前体物排放和气象条件等因素共同驱动，大气臭氧（O3）已成为影响城市夏季环境空气质量的主要污染物.目前物理化学机制驱动的演绎模型在进行O3污染解析时需要的模型参数众多，运算时效性较差；数据驱动的归纳模型运算效率高，但存在可解释性差等问题.通过建立可解释性数据驱动的Correlation-ML-SHAP模型，Correlation模块挖掘O3浓度关联影响因素，机器学习ML模块耦合可解释性SHAP模块计算各驱动因素对O3浓度的影响贡献，实现对驱动因素的定量解析，并以晋城市2021年夏季O3污染过程为例开展应用研究.结果表明，Correlation-ML-SHAP模型能够挖掘并利用强驱动因素模拟O3浓度和量化影响贡献，其中ML模块采用XGBoost模型模拟准确度最佳.2021年夏季晋城市O3污染强驱动因素为：气温、日照强度、湿度和前体物排放水平，贡献权重为：32.1%、21.3%、16.5%和15.6%，其中气温、日照强度和前体物排放水平贡献在污染天分别提升3.4%、1.2%和1.2%，前体物排放贡献权重在污染天排名提升至第3.各驱动因素对O3浓度呈非线性交互影响，气温超24℃或湿度低于70%，分别有94.9%和94.1%的概率对O3污染呈正贡献，这种气象条件下ρ（NO2）超9 μg ·m-3或ρ（CO）超0.7 mg ·m-3，分别有94.9%和99.3%的概率对O3污染呈正贡献.东南风风速低于5.8m ·s-1或南风风速低于5.3m ·s-1，均对O3污染呈正贡献.模型定量解析了各驱动因素对城市O3浓度的影响贡献，可为夏季城市大气O3污染防控提供基础依据. |
| 英文摘要 |
| Driven by precursor emissions, meteorological conditions, and other factors, atmospheric ozone (O3) has become the main pollutant affecting urban air quality in summer. The current deductive models driven by physical and chemical mechanisms require a large number of parameters for the analysis of O3 pollution, and the calculation timeliness is poor. The data-driven inductive models are efficient but have problems such as poor explanation. In this study, an explainable model of data-driven Correlation-ML-SHAP was established to reveal the strongly correlated influencing factors of O3 concentration. Additionally, the machine learning ML module coupled with the explainable SHAP module was used to calculate the contributions of driving factors to O3 concentration, so as to realize the quantitative analysis of driving factors. The O3 pollution process in the summer of 2021 in Jincheng City was used as an example to carry out the application research. The results showed that the Correlation-ML-SHAP model could reveal and use strong driving factors to simulate O3 concentration and quantify influence contribution, and the ML module used the XGBoost model to achieve the best simulation accuracy. Air temperature, solar radiation, relative humidity, and precursor emission level were the strong driving factors of O3 pollution in Jincheng City in summer 2021, and the contribution weights were 32.1%, 21.3%, 16.5%, and 15.6%. The contribution weights of air temperature, solar radiation, and precursor emission level increased by 3.4%, 1.2%, and 1.2% on polluted days, respectively, and the contribution weights of precursor emission level rose to third place on polluted days. Each driving factor had a nonlinear interaction effect on O3 concentration. When the air temperature exceeded 24℃, or the relative humidity was lower than 70%, there was a 94.9% and 94.1% probability of positive contribution to O3 pollution, respectively. Under such meteorological conditions, ρ(NO2) exceeded 9 μg·m-3, or ρ(CO) exceeded 0.7 mg·m-3, and there was a 94.9% and 99.3% probability of positive contribution to O3 pollution, respectively. The southeast wind speed was lower than 5.8 m·s-1, or the south wind speed was lower than 5.3 m·s-1, both of which contributed positively to O3 pollution. The model quantitatively analyzed the influence contribution of various driving factors on urban O3 concentration, which could provide a basis for the prevention and control of urban atmospheric O3 pollution in summer. |
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